Machine vision
Video Processing and Communications
Video Processing and Communications
Wavelet packets-based digital watermarking for image verification and authentication
Signal Processing - Special section: Security of data hiding technologies
Robust image authentication using content based compression
Multimedia Systems
Content-Based Digital Signature for Motion Pictures Authentication and Content-Fragile Watermarking
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
On the design of content-based multimedia authentication systems
IEEE Transactions on Multimedia
Authentication with distortion criteria
IEEE Transactions on Information Theory
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
Design and statistical analysis of a hash-aided image watermarking system
IEEE Transactions on Image Processing
A robust image authentication method distinguishing JPEG compression from malicious manipulation
IEEE Transactions on Circuits and Systems for Video Technology
On the music content authentication
Proceedings of the 20th ACM international conference on Multimedia
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Content-based image authentication typically assesses authenticity based on a distance measure between the image to be tested and its original. Commonly employed distance measures such as the Minkowski measures (including Hamming and Euclidean distances) may not be adequate for content-based image authentication since they do not exploit statistical and spatial properties in features. This paper proposes a feature distance measure for content-based image authentication based on statistical and spatial properties of the feature differences. The proposed statistics- and spatiality-based measure (SSM) is motivated by an observation that most malicious manipulations are localized whereas acceptable manipulations result in global distortions. A statistical measure, kurtosis, is used to assess the shape of the feature difference distribution; a spatial measure, the maximum connected component size, is used to assess the degree of object concentration of the feature differences. The experimental results have confirmed that our proposed measure is better than previous measures in distinguishing malicious manipulations from acceptable ones.